Analysis of food system drivers of deforestation highlights foreign direct investments and urbanization as threats to tropical forests

Approximately 90% of global forest cover changes between 2000 and 2018 were attributable to agricultural expansion, making food production the leading direct driver of deforestation. While previous studies have focused on the interaction between human and environmental systems, limited research has explored deforestation from a food system perspective. This study analyzes the drivers of deforestation in 40 tropical and subtropical countries (2004–2021) through the lenses of consumption/demand, production/supply and trade/distribution using Extreme Gradient Boosting (XGBoost) models. Our models explained a substantial portion of deforestation variability globally (R2 = 0.74) and in Asia (R2 = 0.81) and Latin America (R2 = 0.73). The results indicate that trade- and demand-side dynamics, specifically foreign direct investments and urban population growth, play key roles in influencing deforestation trends at these scales, suggesting that food system-based interventions could be effective in mitigating deforestation. Conversely, the model for Africa showed weaker explanatory power (R2 = 0.30), suggesting that factors beyond the food system may play a larger role in this region. Our findings highlight the importance of targeting trade- and demand-side dynamics to reduce deforestation and how interventions within the food system could synergistically contribute to achieving sustainable development goals, such as climate action, life on land and zero hunger.

The three concentric circles form a simplified representation of the food system.The outermost circle, "Food System Activities," encapsulates food system activities and actors related to the production, processing, distribution, preparation, consumption and disposal of food 7 .The middle circle, "Food Environment," encompasses the "physical, economic, political and socio-cultural context in which consumers engage with the food system to acquire, prepare and consume food" 7 .The innermost circle, "Consumer Behavior," represents "the choices made by consumers, at household or individual levels, on what food to acquire, store, prepare and eat, and on the allocation of food within the household (including gender repartition, feeding of children)" 7 .This nested structure implies that food system activities and elements frame the conditions of the food environment, which, in turn, shapes consumer choices.The different types of food system outcomes are shown on the right.The diagram was adapted from Béné et al. 24 .

Figure S2 .
Figure S2.Percentage of gross tree cover loss (TCL) from Terra-i data between 2005 and 2021, relative to FAO-reported forest area in 2004 for countries included in the analysis (Source: https://data.worldbank.org/indicator/AG.LND.FRST.ZS?view=chart).

Figure S3 .
Figure S3.Spearman correlations in the time series database for production/supply, trade/distribution and consumption/demand variables at the global level.Blue cells represent a positive correlation while red represent a negative one.The larger the correlation, the darker the color.White cells mean nonsignificant correlations.The first row depicts the tested correlations for the response variable (tree cover loss).'Foreign invest' corresponds to cross-sectoral foreign direct investments (FDI).

Figure S4 .
Figure S4.Spearman correlations in the time series database for production/supply, trade/distribution and consumption/demand variables at the Africa level.Blue cells represent a positive correlation, while red represent a negative one.The larger the correlation, the darker the color.White cells mean nonsignificant correlations.The first row depicts the tested correlations for the response variable (tree cover loss).'Foreign invest' corresponds to cross-sectoral foreign direct investments (FDI).

Figure S5 .
Figure S5.Spearman correlations in the time series database for production/supply, trade/distribution and consumption/demand variables at Asia & Oceania level.Blue cells represent a positive correlation, while red represent a negative one.The larger the correlation, the darker the color.White cells mean non-significant correlations.The first row depicts the tested correlations for the response variable (tree cover loss).'Foreign invest' corresponds to cross-sectoral foreign direct investments (FDI).

Figure S6 .
Figure S6.Spearman correlations in the time series database for production/supply, trade/distribution and consumption/demand variables at the Latin America and the Caribbean level.Blue cells represent a positive correlation, while red represent a negative one.The larger the correlation, the darker the color.White cells mean non-significant correlations.The first row depicts the tested correlations for the response variable (tree cover loss).'Foreign invest' corresponds to cross-sectoral foreign direct investments (FDI).

Figure S7 .
Figure S7.Mean and standard deviation of driver variables in Africa.Consists of data for 17 countries.

Figure S8 .
Figure S8.Mean and standard deviation of driver variables in Asia and Oceania.Consists of data for 9 countries in Asia and 2 in Oceania.

Figure S9 .
Figure S9.Mean and standard deviation of driver variables in Latin America and the Caribbean.Consists of data for 12 countries.

Table S1 .
Countries included in the analysis.

Table S2 .
Final hyperparameter settings of all XGBoost models executed with random search procedure.Model indicates the geographic level (Asia = Asia & Oceania; LAC = Latin America and the Caribbean).Each run corresponds to the best-performing model selected from 100 models initialized with random parameter settings (with five runs per geographic level, which were averaged to yield final results).R² is the adjustment/performance measure.RMSE is the Root Mean Squared Error, a performance measure and selection criterion.The hyperparameters include eta (learning rate), max_depth (maximum tree depth), gamma (minimum loss reduction), minimum child weight, colsample_bytree (subsample ratio of columns), subsample (subsample ratio of training instances) and nrounds (number of boosting iterations).